The present invention is directed to multi-resolution contrast-based dynamic range management (MCDRM), and more particularly to a method, system and computer readable medium for implementing and performing a multi-resolution contrast-based dynamic range management algorithm.
Digital x-ray imaging is a well-known, non-contact technique for observing, in real time, interior aspects of an object. In practice, an x-ray beam is generated and targeted on an object of interest. A detecting device is positioned on the other side of the object and detects the x-rays transmitted through the object. The detected x-ray signals are converted to digital signals that represent various features in the object, are further processed, and the resulting signal is displayed on an image display device such as a CRT.
One of the fundamental image processing problems in digital x-ray imaging is the need to transform the intensity dynamic range of the input image to the dynamic range supported by an available display device. Typically, the intensity dynamic range of an image exceeds the dynamic range of the display mechanism by several times. The objective of the transformation accordingly is to compress the “DC” or mean component of the different regions comprising the image so that the dynamic range (typically 256 gray levels) of an available display device may be utilized in a fashion which maximizes the displayed image contrast taking into account, as well, the generally recognized limitation of the human eye of discerning only 256 gray levels.
Common approaches to achieving such transformations are known as “unsharp masking” and “extended dynamic range (EDR)”, the latter a special adaptation of the former. The conventional approach to performing the EDR algorithm, in general, is simply to subtract a portion of the mean from the input signal. However, in some situations, this approach results in important contrast (higher frequency) information either being removed from the image or being artificially enhanced and thereby introducing artifacts. This and other conventional approaches to contrast suffer from image artifacts such as “blooming” and “halos.” A blooming artifact is where the image is saturated at a maximum intensity and lacks texture. In the prior art, blooming artifacts can occur in lung regions and are mapped to 255 (in a 0–255 contrast scale) and appear white due to the inability to properly compress the region. Halos are visualized as bright highlighting about dark objects, such as a contrast filled vessel. Halos are a result of an inaccurate mean estimate coupled with the high pass nature of a dynamic range management computation.
Another problem with the current EDR processing algorithm is that of inconsistent contrast management resulting in exaggeration of negative contrast regions in the image. When a region in the image, such as a vessel filled with dye, for example, has an image intensity which is less than the surrounding local mean intensity value, EDR processing may exaggerate the negative contrast associated with the darker region when it subtracts the local mean intensity values from the intensity values associated with the darker region. This exaggerated negative contrast may result in artifacts, which can lead to misdiagnosis.
Where an image includes multiple areas having potentially differing mean levels of gray, or when images of objects embedded in such areas have respective, different gray levels, or if respective gray levels of an object and its background have similar values, the contrast parameters of the display window must be adjusted to enhance the visibility of these differences in order to obtain a diagnosis of the underlying structure being imaged. Thus, when a viewer's attention is shifted from one object to another, where the contrast of one combination of object and background differs significantly from the contrast of another combination of object and background, various display window parameters relating to contrast adjustment must be changed. Without such adjustments, the image will appear either excessively faint or excessively bright, such that all detail critical to an effective diagnosis is absent. As a result, in order to obtain a diagnosis, it is often necessary, during the course of shifting attention among areas of differing contrast, for the physician to make numerous contrast adjustments to the display window. This can be disruptive in, e.g., radiology and mammography procedures and is altogether unacceptable in cardiac procedures, underway at the time.
Contrast of a cardiac digital X-ray image must be managed in a deterministic and consistent manner to achieve optimum results. For example, cardiologists perform diagnoses by examining the apparent thickness of a coronary artery, as revealed in X-ray imaging by a contrast medium injection. Because of X-ray physics, the artery thickness may lead to a modulation of the underlying background gray-level, i.e., contrast. Contrast consistency, particularly in dye filled vessels, is important, since processing-induced contrast changes in vessels may be interpreted as coronary disease leading to misdiagnosis. Consequently, it is desirable that artery contrast (i.e., not intensity) pass through DRM processing with deterministic and linear gain. However, minimal and deterministic modification to contrast can be tolerated, particularly in X-ray imaging of the lungs, where there is significant compression of the mean and minimal clinically relevant information. In fluoroscopic mode, the cardiologist is focused on the placement of interventional devices, and the rendering of the corresponding contrast is more directed by the visibility of these tools. In prior systems, however, contrast functions were managed in a non-linear manner, which varied with the local mean—leading to artifacts and confusion and resulting, in at least some cases, in increased patient exposure in efforts to position the X-ray tube and an image intensifier such that a vasculature of interest, e.g., over a spine diagram, could be viewed satisfactorily.
Accordingly, it is desirable to provide a compression algorithm that overcomes the deficiencies of the aforementioned EDR processing algorithms and which permits managing the mean (low frequency) and contrast (high frequency) content of an image, separately and directly. Further, it is desirable to provide a compression algorithm that overcomes problems associated with inconsistent contrast management. Yet other limitations exist in even the more current EDR processing algorithm. For example, an X-ray imaging system is generally provided with controllable settings that allow the user to manually select one of three dynamic ranges. The EDR processor then subtracts a particular percentage of the local mean intensity value from the input image intensity value, based on the setting selected by the user. If the user fails to select the appropriate setting which best accommodates the dynamic range of a given image, the displayed image may have poor image quality. In many cases, this may result in the loss of more high frequency contrast information than is necessary to perform the compression.
It accordingly is desirable to provide a compression algorithm which adaptively adjusts to the dynamic range of the image, so that high frequency contrast information is preserved, while applying minimal compression to display the image in a more deterministic manner and with reduced complexity. It is also desirable that the method of dynamic range management avoid the introduction of image artifacts such as halos and blooming.